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It is very important. Incorrect conclusions can be reached when the sample does not represent the underlying population. Experimental studies frequently go to great lengths to insure an unbiased sample. In observational studies, the statistician may identify factors which could make his sample not representative of the population. I will give you a real example. The US Fish and Wildlife Division conducted a study of the area that Florida cougars roam the Everglades. They tagged and tracked the movements by GPS. By using only daytime data in their computer models, a time when the cougars were more likely to sleep, they underestimated the distance the cougars could roam. You may be able to find many examples of biasing the data, either at the collection stage or later culling out certain data (as was done in the cougar example).

Q: Immportance of representative sample in statistical inference?

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It is quite likely that the sample is not representative of the population and so while statistical conclusion may be valid for the sample, they may not apply to the population.

Because without representative sample, your results will not be valid.

In general the mean of a truly random sample is not dependent on the size of a sample. By inference, then, so is the variance and the standard deviation.

The larger the sample of data collected leads to a more accurate conclusion.

A sample that accurately reflects the characteristics of the population as a whole

Related questions

The purpose of statistical inference is to obtain information about a population form information contained in a sample.

Many statistical statements for a population which are based on a sample are not valid if the sample is not representative.

Statistical inference is a conclusion about the value of a population parameter based on information from the corresponding sample statistic and the associated probability distribution.

statistical inference

Statistical sampling is an objective approach using probability to make an inference about the population. The method will determine the sample size and the selection criteria of the sample. The reliability or confidence level of this type of sampling relates to the number of times per 100 the sample will represent the larger population. Non-statistical sampling relies on judgment to determine the sampling method,the sample size,and the selection items in the sample.

The purpose is to obtain a statistical representative sample from the material to be tested.

It is quite likely that the sample is not representative of the population and so while statistical conclusion may be valid for the sample, they may not apply to the population.

Non-probability or Judgement Samples has to do with a basic researcher assumptions about the nature of the population, the researcher assumes that any sample would be representative to the population,the results of this type of samples can not be generalized to the population(cause it may not be representative as the research assumed) and the results may be biased. Probability or Random samples is a sample that to be drawn from the population such that each element in the population has a chance to be in the selected sample the results of the random samples can be used in Statistical inference purposes

Actually inference is based upon statistical evaluation of data. Inference is a generalization made about a defined "population" from data obtained from one or more "samples". Each member of the population must have an equal chance of being included into a sample. An example would be when a number of people from a particular state are asked who they would vote for. The "inference" would be a generalization that the state favors a particular candidate 55% TO 45%. The validity of the inference would depend upon sample size and how true the sampling was to giving every member of the sampled population (the entire state) an equal chance to be included in the sample.

A sample should be representative of the population it is drawn from, have enough data points to provide reliable conclusions, and be selected randomly or systematically to minimize bias. Additionally, samples should be sufficiently large to ensure statistical significance.

A representative sample is a randomly selected subset of the population.

Because without representative sample, your results will not be valid.